An improved global-best harmony search algorithm for faster optimization

In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.     

A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm

Steganography is knowledge and art of hiding secret data into information which is largely used in information security systems. Various methods have been proposed in the literature which most of them are not capable of both preventing visual degradation and providing a large embedding capacity. In this paper, we propose a tunable visual image quality and data lossless method in spatial domain based on a genetic algorithm (GA). The main idea of the proposed technique is modeling the steganography problem as a search and optimization problem. Experimental results, in comparison with other currently popular steganography techniques, demonstrate that the proposed algorithm not only achieves high embedding capacity but also enhances the PSNR of the stego image.     

Nonlinear time series forecasting with Bayesian neural networks

The Bayesian learning provides a natural way to model the nonlinear structure as the artificial neural networks due to their capability to cope with the model complexity. In this paper, an evolutionary Monte Carlo (MC) algorithm is proposed to train the Bayesian neural networks (BNNs) for the time series forecasting. This approach called as Genetic MC is based on Gaussian approximation with recursive hyperparameter. Genetic MC integrates MC simulations with the genetic algorithms and the fuzzy membership functions. In the implementations, Genetic MC is compared with the traditional neural networks and time series techniques in terms of their forecasting performances over the weekly sales of a Finance Magazine.     

Credit rating with a monotonicity-constrained support vector machine model

Deciding whether borrowers can fulfill their obligations is a major issue for financial institutions, and while various credit rating models have been developed to help achieve this, they cannot reflect the domain knowledge of human experts. This paper proposes a new rating model based on a support vector machine with monotonicity constraints derived from the prior knowledge of financial experts. Experiments conducted on real-world data sets show that the proposed method, not only data driven but also domain knowledge oriented, can help correct the loss of monotonicity in data occurring during the collecting process, and performs better than the conventional counterpart.     

国产耐火砖在德士古水煤浆气化炉的使用

分析气化炉耐火材料损蚀原因及国产耐火砖在德士古水煤浆加压气化炉中的使用情况，使用结果证明，国产耐火砖可以替代进口砖，每年节约设备投资600万元左右。     

一种功能性化妆品原料--透明质酸

论述了透明质酸的理化与生物特性、简易制备方法、特殊功能及其在化妆品中的应用，同时提供了几个常用的化妆品基础配方。透明质酸是一种对人体具有一系列特殊保护功能且性能优良的天然酸性黏多糖物质。它用于化妆品，可起到保湿护肤、抗衰老、营养皮肤、促进伤口愈合、抗菌消炎及稳定乳化等功能。展望了其在今后国内日化市场上的发展前景，为国内透明质酸资源的充分开发利用提供一些有益信息。     

基于交叉突变算子的人工蜂群算法及其应用

人工蜂群（artificial bee colony,ABC）算法在解决多峰函数优化问题时经常会陷入局部最优,使得算法过早停滞,而在解决单峰问题时往往出现收敛速度过慢的问题。针对上述不足,为了进一步提高算法的优化性能,提出了一种基于交叉突变的人工蜂群（intersect mutation ABC,IMABC）算法。IMABC算法将整个蜂群依据其适应度值优劣进行划分,引入种群划分参数,对不同种群中的个体运用交叉突变算子,有效地平衡了种群的局部开采与全局探测能力,避免早熟收敛和提高收敛速度。从对基本函数的测试上可以看出,IMABC相对于GABC、IABC、ABC/best等改进的ABC算法,优化能力有了较大的提高。最后,将IMABC用于优化K-means算法,验证了该方法具有一定的实用性。     

丙烯酸酯改性双环戊二烯酚型环氧树脂及其性能研究

通过FT－IR光谱表征了丙烯酸酯改性的DCPD酚型环氧树脂／DDM体系结构，并通过测定改性体系的凝胶时间和分析凝胶时间的影响因素，研究了该体系的反应特性，同时，对改造前后DCPD酚型环氧树脂体系的性能做了分析。     

基于N步不变集的时滞切换系统的饱和控制

构造离散时滞切换系统的不变集,提出基于N步不变集的切换控制器设计方法,估计执行器饱和非线性的吸引域范围。首先,考虑时滞的影响,选取依赖于时滞的Lyapunov函数,构造时滞切换系统的不变集,并将其表达为若干个椭球集的凸组合,椭球集的个数与时滞常数相关。其次,在系统的前N个采样时刻,分别施加不同的饱和约束,求解得到一组椭球集,椭球集的个数与常数N相关,而每一步计算得到的椭球集均为时滞切换系统的不变集。再将N个不变集用一组凸包系数拟合,即可获取较大的吸引域估计。最后,在满足平均驻留时间约束的条件下设计切换律,并设计状态反馈控制器,保证闭环系统渐近稳定。控制器的求解转化为线性矩阵不等式的可行性问题。仿真结果验证了所提方法的可行性和有效性。     

结合相关规则和本体加权图的查询扩展

针对现有信息检索系统查询性能的不足,提出了一种结合相关规则和WordNet本体信息的查询扩展方法.该方法借助相关规则挖掘和WordNet本体信息构建加权词语关系图,并根据加权图的结构和权重信息计算扩展词的重要性.查询时,从这个图中取原查询词的最邻近词作为扩展词来源,选取其中权重最大的p个词返回并进行二次检索.在实现算法的基础上,通过Lucene全文检索器进行实验,将所得的结果值F1与其他算法的结果作比较.比较结果表明,该方法比不作扩展的检索有约16.93％的性能提升.